Customers of electric utility companies sometimes commit fraud by stealing electricity without paying for it. This is a significant problem for the electric utility companies, as the detection of fraud on a widespread scale is difficult.
Traditionally, electric utility companies have relied on one of three methods for detecting fraud: (1) a human analyst with deep subject matter expertise and access to a diverse set of a data sources, such as housing permits; (2) a rules-based system provided by the manufacturer of the electric metering technology; or (3) a luck-based system, in which other customers notify the electric utility company of obvious cases of fraud (e.g., their neighbor is clearly stealing electricity). These methods are unreliable, which is evidenced by the majority of fraud cases being identified through the luck-based system.
The unreliability of these methods stems from a combination of low detection rates and high false-positive rates. Some estimates place the false positive rate of these traditional methods at 80%, such that the vast majority of detected fraud cases are not actually occurrences of fraud. This has resulted in a large expense for electric utility companies, to the point where catching fraud is no longer beneficial from a cost/benefit standpoint. This economic infeasibility of catching fraud has further compounded the problem by emboldening existing fraudsters and encouraging potential fraudsters to commit fraud.
Thus, there exists a need for a reliable, electricity fraud detection system that can detect fraud without low detection rates and high false-positive rates and which is sufficiently robust to be applied to both residential and industrial electricity fraud detection. Embodiments of the present disclosure are directed to addressing at least these needs.